Explaining the intentions to share and reuse knowledge in the context of IT service operations
Bibliographic record
Abstract
Purpose Aims to provide an understanding on IS/IT professionals' intentions to share and reuse knowledge in the context of information technology service operations. Design/methodology/approach The theory of planned behavior (TPB) is applied for examining IS/IT professionals' intention to share and reuse knowledge. The data were collected from working IS/IT professionals using an online survey, and partial least squares was used for analyzing the data. Findings The results from this study indicate that the theory of planned behavior is an adequate model for investigating behavioral intentions of knowledge sharing and reuse in the context of information technology service operations. All direct determinants of intention to share knowledge, except subjective norm regarding information technology service operations knowledge sharing, and intention to reuse knowledge were significant. Research limitations/implications This paper is one of the first to attempt to study both knowledge sharing and knowledge reuse under the same context. The relatively small sample size has limited statistical power of the implications drawn. Practical implications This paper attempts to highlight the importance of information technology service operations in the IS/IT industry, and study knowledge management in that context. To encourage knowledge sharing, top management is advised that they should focus on building up a positive attitude in their employees, through improving relationships and recognition of their contributions. Originality/value This paper is the first attempt to combine both knowledge sharing and knowledge reuse in the same context, and initiates research in the area of information technology service operations. This paper offers help to both practitioners and researchers in understanding in that area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".